We can see from the histogram on the left that the bin width is too small because it shows too much individual data and does not allow the underlying pattern (frequency distribution) of the data to be easily seen. Consider the histogram we produced earlier (see above): the following histograms use the same data, but have either much smaller or larger bins, as shown below: You need to make sure that the bins are not too small or too large. There is no right or wrong answer as to how wide a bin should be, but there are rules of thumb.

This is because a histogram represents a continuous data set, and as such, there are no gaps in the data (although you will have to decide whether you round up or round down scores on the boundaries of bins). Notice that, unlike a bar chart, there are no 'gaps' between the bars (although some bars might be 'absent' reflecting no frequencies). For the above data set, the frequencies in each bin have been tabulated along with the scores that contributed to the frequency in each bin (see below): Each bin contains the number of occurrences of scores in the data set that are contained within that bin. In the example above, age has been split into bins, with each bin representing a 10-year period starting at 20 years. To construct a histogram from a continuous variable you first need to split the data into intervals, called bins. How do you construct a histogram from a continuous variable? An example of a histogram, and the raw data it was constructed from, is shown below: This allows the inspection of the data for its underlying distribution (e.g., normal distribution), outliers, skewness, etc. A histogram is a plot that lets you discover, and show, the underlying frequency distribution (shape) of a set of continuous data.

Histogram maker statistics